This invention relates to array based computing devices. More particularly, this invention relates to a network for configuration of multiple context processing elements.
Advances in semiconductor technology have greatly increased the processing power of a single chip general-purpose computing device. The relatively slow increase in inter-chip communication bandwidth requires modern high performance devices to use as much of the potential on-chip processing power as possible. This results in large, dense integrated circuit devices and a large design space of processing architectures. This design space is generally viewed in terms of granularity, wherein granularity dictates that designers have the option of building very large processing units, or many smaller ones, in the same silicon area. Traditional architectures are either very coarse grain, like microprocessors, or very fine grain, like field programmable gate arrays (FPGAs).
Microprocessors, as coarse grain architecture devices, incorporate a few large processing units that operate on wide data words, each unit being hardwired to perform a defined set of instructions on these data words. Generally, each unit is optimized for a different set of instructions, such as integer and floating point, and the units are generally hardwired to operate in parallel. The hardwired nature of these units allows for very rapid instruction execution. In fact, a great deal of area on modern microprocessor chips is dedicated to cache memories in order to support a very high rate of instruction issue. Thus, the devices efficiently handle very dynamic instruction streams.
Most of the silicon area of modern microprocessors is dedicated to storing data and instructions and to control circuitry. Therefore, most of the silicon area is dedicated to allowing computational tasks to heavily reuse the small active portion of the silicon, the arithmetic logic units (ALUs). Consequently very little of the capacity inherent in a processor gets applied to the problem; most of the capacity goes into supporting a high diversity of operations.
Field programmable gate arrays, as very fine grain devices, incorporate a large number of very small processing elements. These elements are arranged in a configurable interconnected network. The configuration data used to define the functionality of the processing units and the network can be thought of as a very large semantically powerful instruction word allowing nearly any operation to be described and mapped to hardware.
Conventional FPGAs allow finer granularity control over processor operations, and dedicate a minimal area to instruction distribution. Consequently, they can deliver more computations per unit of silicon than processors, on a wide range of operations. However, the lack of resources for instruction distribution in a network of prior art conventional FPGAs make them efficient only when the functional diversity is low, that is when the same operation is required repeatedly and that entire operation can be fit spatially onto the FPGAs in the system.
Furthermore, in prior art FPGA networks, retiming of data is often required in order to delay data. This delay is required because data that is produced by one processing element during one clock cycle may not be required by another processing element until several clock cycles after the clock cycle in which it was made available. One prior art technique for dealing with this problem is to configure some processing elements to function as memory devices to store this data. Another prior art technique configures processing elements as delay registers to be used in the FPGA network. The problem with both of these prior art technique is that valuable silicon is wasted by using processing elements as memory and delay registers.
Dynamically programmable gate arrays (DPGAs) dedicate a modest amount of on-chip area to store additional instructions allowing them to support higher operational diversity than traditional FPGAs. However, the silicon area necessary to support this diversity must be dedicated at fabrication time and consumes area whether or not the additional diversity is required. The amount of diversity supported, that is, the number of instructions supported, is also fixed at fabrication time. Furthermore, when regular data path operations are required all instruction stores are required to be programmed with the same data using a global signal broadcast to all DPGAs.
The limitations present in the prior art FPGA and DPGA networks in the form of limited control over configuration of the individual FPGAs and DPGAs of the network severely limits the functional diversity of the networks. For example, in one prior art FPGA network, all FPGAs must be configured at the same time to contain the same configurations. Consequently, rather than separate the resources for instruction storage and distribution from the resources for data storage and computation, and dedicate silicon resources to each of these resources at fabrication time, there is a need for an architecture that unifies these resources. Once unified, traditional instruction and control resources can be decomposed along with computing resources and can be deployed in an application specific manner. Chip capacity can be selectively deployed to dynamically support active computation or control reuse of computational resources depending on the needs of the application and the available hardware resources.
A method and an apparatus for configuration of multiple context processing elements (MCPEs)are described. According to one aspect of the invention, the structure that joins the MCPE cores into a complete array in one embodiment is actually a set of several mesh-like interconnect structures. Each interconnect structure forms a network, and each network is independent in that it uses different paths, but the networks join at MCPE input switches. The network structure of one embodiment of the present invention is comprised of a local area broadcast network (level 1), a switched interconnect network (level 2), a shared bus network (level 3), and a broadcast network. In one embodiment, the level 3 network is used to carry configuration data for the MCPEs while the broadcast network is used to carry configuration data for the level 3 network drivers and switches. In one embodiment, the level 3 network is bidirectional and dynamically routable.
Each multiple context processing element in a networked array of multiple context processing elements has an assigned physical identification. This physical identification may be assigned at the time of network development. Virtual identifications may also be assigned to a number of the multiple context processing elements. Data is transmitted to at least one of the multiple context processing elements of the array. The data comprises control data, configuration data, an address mask, and a destination identification. The transmitted data is also used to select whether the physical identification or the virtual identification will be used to select multiple context processing elements for manipulation.
The transmitted address mask is applied to the physical or virtual identification and to a destination identification. The masked physical or virtual identification is compared to the masked destination identification. When the masked physical or virtual identification of a multiple context processing element matches the masked destination identification, at least one of the number of multiple context processing elements are manipulated in response to the transmitted data. Manipulation comprises programming a multiple context processing element with at least one configuration memory context and selecting a configuration memory context to control the functioning of the multiple context processing element. The manipulation may occur while the multiple context processing element is executing a present function. The manipulated multiple context processing elements define at least one region of the networked array, the region having an arbitrary shape.